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GISTEMP v4 Figures
GISS Surface Temperature Analysis (GISTEMP v4)
The GISS Surface Temperature Analysis ver. 4 (GISTEMP v4) is an estimate of global surface temperature change.Graphs and tables are updated around the middle of every month using current data files from NOAA GHCNv4 (meteorological stations) and ERSST v5 (ocean areas), combined asdescribed in our publicationsHansen et al. (2010) andLenssen et al. (2019).These updated files incorporate reports for the previous month and also late reportsand corrections for earlier months.
News and Updates
September 18, 2020: The 'Global Maps' and 'Zonal Means' utilities may now be used to show both AIRS v7 and AIRS v6 based maps and graphs. Please note that the AIRS temperature estimates are based on satellite data only and are completely independent of the data used by the GISS analysis.
The August 2020 AIRS data are impacted by a technical satellite problem that wiped out the data from 8/16 to 9/02. We also learned that the November 2003 data are similarly affected by a 2-week data gap. No attempt has been made so far to modify these estimates to account for those data losses.
August 14, 2020:Dr. Joel Susskind's group released a new version 7 of the AIRS temperature data. Hence, we are switching from the earlier v6 data to v7 starting today, which will have small implications for the trends. Comparisons of these versions as well as comparisons to the GISTEMP v4 product will be provided with next month's release. Some comparisons of T-AIRS v6 and GISTEMP v4 are shown at the bottom of Dr. Sato's site.
April 13, 2020:The older versions v3b and v4 of ERSST (ocean temperatures) are no longer updated by NOAA/NCEI. Hence, we will no longer be able to make those data available on our 'Global Maps' and 'Zonal Means' pages.
See theGISTEMP News page for a list of announcements and NASA articles related to the GISTEMP analysis.Announcements when our monthly updates are released are posted to the NASA/GISS Twitter feed.
See theUpdates to Analysis page for detailed update information about GISTEMP v4.
Contacts
Before contacting us, please check if your question about the GISTEMP analysis is already answered inthe FAQ.
If the FAQ does not answer your question,please address your inquiry toDr. Reto Ruedy.
Other researchers participating in the GISTEMP analysis areMichael Hendrickson,Dr. Makiko Sato,andDr. Ken Lo.This research was initiated by Dr. James E. Hansen, now retired.It is currently led by Dr. Gavin Schmidt.
Citation
When referencing the GISTEMP v4 data provided here, please cite both this webpage and also our most recentscholarly publication about the data.In citing the webpage, be sure to include the date of access.
- GISTEMP Team, 2020:GISS Surface Temperature Analysis (GISTEMP), version 4. NASA Goddard Institute for Space Studies.Dataset accessed 20YY-MM-DD at https://data.giss.nasa.gov/gistemp/.
- Lenssen, N., G. Schmidt, J. Hansen, M. Menne, A. Persin, R. Ruedy, and D. Zyss, 2019: Improvements in the GISTEMP uncertainty model. J. Geophys. Res. Atmos., 124, no. 12, 6307-6326, doi:10.1029/2018JD029522.
Background of the GISS Analysis
The basic GISS temperature analysis scheme was defined in the late 1970s by James Hansen when a methodof estimating global temperature change was needed for comparison with one-dimensional global climatemodels. The scheme was based on the finding that the correlation of temperature change was reasonablystrong for stations separated by up to 1200 km, especially at middle and high latitudes. This factproved sufficient to obtain useful estimates for global mean temperature changes.
Temperature analyses were carried out prior to 1980, notably those of Murray Mitchell, but most coveredonly 20-90°N latitudes. Our first published results (Hansen et al. 1981) showed that, contrary toimpressions from northern latitudes, global cooling after 1940 was small, and there was net globalwarming of about 0.4°C between the 1880s and 1970s.
The early analysis scheme went through a series of enhancements that are listed and illustrated on theHistory Page.
Documentation and Assessment of Results
The analysis method was fully documented in Hansenand Lebedeff (1987), including quantitative estimates of the error in annual and 5-year meantemperature change. This was done by sampling at station locations a spatially complete data set of along run of a global climate model, which was shown to have realistic spatial and temporal variability.This however only addresses the error due to incomplete spatial coverage of measurements. A more completeuncertainty analysis was published in Lenssen et al. (2019) and forms the core of the Uncertainty website.
As there are other potential sources of error, such as urban warming near meteorological stations, manyother methods have been used to verify the approximate magnitude of inferred global warming. Thesemethods include inference of surface temperature change from vertical temperature profiles in the ground(bore holes) at many sites around the world, rate of glacier retreat at many locations, and studies byseveral groups of the effect of urban and other local human influences on the global temperature record.All of these yield consistent estimates of the approximate magnitude of global warming, which reachedabout 0.8°C in 2010, twice the magnitude reported in 1981.
![Temp Temp](https://upload.wikimedia.org/wikipedia/commons/thumb/0/07/Oscilloscopic_tube.jpg/220px-Oscilloscopic_tube.jpg)
Further affirmation of the reality of the warming is its spatial distribution, which has largest valuesat locations remote from any local human influence, with a global pattern consistent with that expectedfor response to global climate forcings (larger in the Northern Hemisphere than the Southern Hemisphere,larger at high latitudes than low latitudes, larger over land than over ocean).
An updated documentation (Hansen et al. 2010)compares alternative analyses and addresses questions about perception and reality of global warming;various choices for the ocean data are tested; it is also shown that global temperature change issensitive to estimated temperature change in polar regions, where observations are limited. A multi-yearsmoothing is applied to fully remove the annual cycle and improve information content in temperaturegraphs. Musiclab realrick v4 0 download free. Despite large year-to-year fluctuations associated with the El Niño-La Niña cycle oftropical ocean temperature, the conclusion could be made that global temperature continued to riserapidly in the 21st century, new record heights being reached in every decade.
GISS Homogenization (Urban Adjustment)
One of the improvements — introduced in 1998 — was the implementation of a method to addressthe problem of urban warming: The urban and peri-urban (i.e., other than rural) stations are adjusted sothat their long-term trend matches that of the mean of neighboring rural stations. Urban stationswithout nearby rural stations are dropped. This preserves local short-term variability without affectinglong term trends. Originally, the classification of stations was based on population size near thatstation; the current analysis uses satellite-observed nightlights to determine which stations are located in urban and peri-urban areas.
Monthly Updates
Graphs and tables are updated around the middle of every month using the current adjusted GHCNdata. The new file incorporates reports for the previous month as well as late reports and correctionsfor earlier months.
We maintain a running record of any modifications made to the analysis on our Updates to Analysis page.
Programs used in the GISTEMP analysis and documentation on their use are available for download. The programs assume a Unix-like operating system and require familiarity with Python for installation and use.
News, Features and Special Topics
Further reading about the GISTEMP analysis is available in the following:
+ NASA news and features related to the GISTEMP analysis
+ Frequently Asked Questions, and Answers
+ The Elusive Absolute Surface Air Temperature
+ Frequently Asked Questions, and Answers
+ The Elusive Absolute Surface Air Temperature
Tables ofGlobal and Hemispheric Monthly Means and Zonal Annual Means
The following are plain-text files in tabular format of temperature anomalies, i.e. deviations from the corresponding 1951-1980 means.
Combined Land-Surface Air and Sea-Surface Water Temperature Anomalies(Land-Ocean Temperature Index, LOTI)
- Global-mean monthly, seasonal, and annual means, 1880-present, updated through most recent month: TXT, CSV
- Northern Hemisphere-mean monthly, seasonal, and annual means, 1880-present, updated through most recent month: TXT, CSV
- Southern Hemisphere-mean monthly, seasonal, and annual means, 1880-present, updated through most recent month: TXT, CSV
- Zonal annual means, 1880-present, updated through most recent complete year: TXT, CSV
Gridded Monthly Temperature Anomaly Data
Users interested in the entire gridded surface air temperature anomaly data maydownload netCDF files containing selected series on a regular 2°×2°grid or the basic SBBX binary files.
Compressed NetCDF Files (regular 2°×2° grid)
- Land-Ocean Temperature Index, ERSSTv5, 1200km smoothing (23 MB)
- Surface air temperature (no ocean data), 250km smoothing (9 MB)
Compressed Basic Subbox Grid Series (equal-area grid)
- Surface air temperature, 1200km smoothing - GISTEMPv4 (27 MB)
- Surface air temperature, 250km smoothing - GISTEMP v4 (9 MB)
- Sea surface air temperature (ERSSTv5), currently used (30 MB)
- Sea surface air temperature (ERSSTv4), used until July 2017 (30 MB)
- Sea surface air temperature (ERSSTv3b), used until June 2015 (30 MB)
- Sea surface air temperature (HadR2), used until Nov. 2012 (30 MB)
Also available are various FORTRAN programs and instructions to create (time series of) regular griddedanomaly maps from the basic files. Be sure to read the README file for discussion of the files' binaryformat.
References
Please see the GISTEMP references page for citations to publicationsrelated to this research.Copies of many of our papers are available in the GISS publications database.
Related Links
+ Frequently Asked Questions
+ GISTEMP History
+ Uncertainty Quantification
+ UN FAOSTAT Temperature Data
Wifi scanner 2 9 4. + NOAA/NCEI State of the Climate
+ Release Dates
Data Sources:
+ Land data: GHCN v4
+ Ocean data: ERSST v5
+ El Nino data
+ AIRS Data
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